Overview

Dataset statistics

Number of variables24
Number of observations58693
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 MiB
Average record size in memory192.0 B

Variable types

Numeric12
Categorical12

Alerts

Age is highly overall correlated with EducationHigh correlation
Brand is highly overall correlated with Incidence and 1 other fieldsHigh correlation
Day is highly overall correlated with Price_3 and 1 other fieldsHigh correlation
Education is highly overall correlated with AgeHigh correlation
Incidence is highly overall correlated with Brand and 1 other fieldsHigh correlation
Income is highly overall correlated with OccupationHigh correlation
Last_Inc_Brand is highly overall correlated with Last_Inc_QuantityHigh correlation
Last_Inc_Quantity is highly overall correlated with Last_Inc_BrandHigh correlation
Occupation is highly overall correlated with IncomeHigh correlation
Price_1 is highly overall correlated with Promotion_1High correlation
Price_2 is highly overall correlated with Promotion_2High correlation
Price_3 is highly overall correlated with Day and 1 other fieldsHigh correlation
Price_4 is highly overall correlated with Day and 2 other fieldsHigh correlation
Promotion_1 is highly overall correlated with Price_1High correlation
Promotion_2 is highly overall correlated with Price_2High correlation
Promotion_4 is highly overall correlated with Price_4High correlation
Quantity is highly overall correlated with Brand and 1 other fieldsHigh correlation
Promotion_3 is highly imbalanced (74.5%)Imbalance
Promotion_5 is highly imbalanced (77.7%)Imbalance
Brand has 44055 (75.1%) zerosZeros
Quantity has 44055 (75.1%) zerosZeros
Last_Inc_Brand has 44133 (75.2%) zerosZeros

Reproduction

Analysis started2024-01-27 14:05:25.512627
Analysis finished2024-01-27 14:05:41.041921
Duration15.53 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

ID
Real number (ℝ)

Distinct500
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0000025 × 108
Minimum2 × 108
Maximum2.000005 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:41.121860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2 × 108
5-th percentile2.0000003 × 108
Q12.0000013 × 108
median2.0000025 × 108
Q32.0000038 × 108
95-th percentile2.0000048 × 108
Maximum2.000005 × 108
Range499
Interquartile range (IQR)250

Descriptive statistics

Standard deviation144.31668
Coefficient of variation (CV)7.2158247 × 10-7
Kurtosis-1.2061825
Mean2.0000025 × 108
Median Absolute Deviation (MAD)125
Skewness-0.0094562892
Sum1.1738615 × 1013
Variance20827.303
MonotonicityIncreasing
2024-01-27T19:35:41.243775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000187 358
 
0.6%
200000041 353
 
0.6%
200000247 347
 
0.6%
200000097 182
 
0.3%
200000297 179
 
0.3%
200000393 179
 
0.3%
200000399 178
 
0.3%
200000345 178
 
0.3%
200000064 175
 
0.3%
200000351 173
 
0.3%
Other values (490) 56391
96.1%
ValueCountFrequency (%)
200000001 101
0.2%
200000002 87
0.1%
200000003 97
0.2%
200000004 85
0.1%
200000005 111
0.2%
200000006 86
0.1%
200000007 83
0.1%
200000008 97
0.2%
200000009 102
0.2%
200000010 98
0.2%
ValueCountFrequency (%)
200000500 124
0.2%
200000499 106
0.2%
200000498 131
0.2%
200000497 120
0.2%
200000496 120
0.2%
200000495 113
0.2%
200000494 138
0.2%
200000493 138
0.2%
200000492 111
0.2%
200000491 119
0.2%

Day
Real number (ℝ)

HIGH CORRELATION 

Distinct730
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.43107
Minimum1
Maximum730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:41.357223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile32
Q1161
median343
Q3530
95-th percentile689
Maximum730
Range729
Interquartile range (IQR)369

Descriptive statistics

Standard deviation212.04506
Coefficient of variation (CV)0.60682943
Kurtosis-1.2165554
Mean349.43107
Median Absolute Deviation (MAD)184
Skewness0.092709478
Sum20509158
Variance44963.107
MonotonicityNot monotonic
2024-01-27T19:35:41.475499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
395 179
 
0.3%
51 173
 
0.3%
18 168
 
0.3%
25 165
 
0.3%
11 161
 
0.3%
58 161
 
0.3%
449 160
 
0.3%
70 159
 
0.3%
117 158
 
0.3%
44 157
 
0.3%
Other values (720) 57052
97.2%
ValueCountFrequency (%)
1 113
0.2%
2 29
 
< 0.1%
3 81
0.1%
4 136
0.2%
5 70
0.1%
6 95
0.2%
7 44
 
0.1%
8 73
0.1%
9 80
0.1%
10 111
0.2%
ValueCountFrequency (%)
730 57
0.1%
729 50
0.1%
728 32
 
0.1%
727 102
0.2%
726 99
0.2%
725 119
0.2%
724 88
0.1%
723 100
0.2%
722 82
0.1%
721 60
0.1%

Incidence
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
44055 
1
14638 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44055
75.1%
1 14638
 
24.9%

Length

2024-01-27T19:35:41.589559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:41.678467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44055
75.1%
1 14638
 
24.9%

Most occurring characters

ValueCountFrequency (%)
0 44055
75.1%
1 14638
 
24.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44055
75.1%
1 14638
 
24.9%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44055
75.1%
1 14638
 
24.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44055
75.1%
1 14638
 
24.9%

Brand
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84430852
Minimum0
Maximum5
Zeros44055
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:41.756427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.6330834
Coefficient of variation (CV)1.9342258
Kurtosis1.368604
Mean0.84430852
Median Absolute Deviation (MAD)0
Skewness1.7141618
Sum49555
Variance2.6669613
MonotonicityNot monotonic
2024-01-27T19:35:41.848556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 44055
75.1%
5 4978
 
8.5%
2 4542
 
7.7%
4 2927
 
5.0%
1 1350
 
2.3%
3 841
 
1.4%
ValueCountFrequency (%)
0 44055
75.1%
1 1350
 
2.3%
2 4542
 
7.7%
3 841
 
1.4%
4 2927
 
5.0%
5 4978
 
8.5%
ValueCountFrequency (%)
5 4978
 
8.5%
4 2927
 
5.0%
3 841
 
1.4%
2 4542
 
7.7%
1 1350
 
2.3%
0 44055
75.1%

Quantity
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69197349
Minimum0
Maximum15
Zeros44055
Zeros (%)75.1%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:41.939084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.498734
Coefficient of variation (CV)2.1658836
Kurtosis11.49525
Mean0.69197349
Median Absolute Deviation (MAD)0
Skewness2.9444606
Sum40614
Variance2.2462037
MonotonicityNot monotonic
2024-01-27T19:35:42.036065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 44055
75.1%
3 4241
 
7.2%
2 3800
 
6.5%
1 3542
 
6.0%
4 1165
 
2.0%
5 921
 
1.6%
6 355
 
0.6%
7 200
 
0.3%
8 133
 
0.2%
9 106
 
0.2%
Other values (6) 175
 
0.3%
ValueCountFrequency (%)
0 44055
75.1%
1 3542
 
6.0%
2 3800
 
6.5%
3 4241
 
7.2%
4 1165
 
2.0%
5 921
 
1.6%
6 355
 
0.6%
7 200
 
0.3%
8 133
 
0.2%
9 106
 
0.2%
ValueCountFrequency (%)
15 2
 
< 0.1%
14 5
 
< 0.1%
13 20
 
< 0.1%
12 29
 
< 0.1%
11 38
 
0.1%
10 81
 
0.1%
9 106
 
0.2%
8 133
 
0.2%
7 200
0.3%
6 355
0.6%

Last_Inc_Brand
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84079873
Minimum0
Maximum5
Zeros44133
Zeros (%)75.2%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:42.129548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.631628
Coefficient of variation (CV)1.940569
Kurtosis1.3911061
Mean0.84079873
Median Absolute Deviation (MAD)0
Skewness1.7211468
Sum49349
Variance2.6622099
MonotonicityNot monotonic
2024-01-27T19:35:42.220801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 44133
75.2%
5 4972
 
8.5%
2 4491
 
7.7%
4 2914
 
5.0%
1 1349
 
2.3%
3 834
 
1.4%
ValueCountFrequency (%)
0 44133
75.2%
1 1349
 
2.3%
2 4491
 
7.7%
3 834
 
1.4%
4 2914
 
5.0%
5 4972
 
8.5%
ValueCountFrequency (%)
5 4972
 
8.5%
4 2914
 
5.0%
3 834
 
1.4%
2 4491
 
7.7%
1 1349
 
2.3%
0 44133
75.2%

Last_Inc_Quantity
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
44133 
1
14560 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 44133
75.2%
1 14560
 
24.8%

Length

2024-01-27T19:35:42.322926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:42.405754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 44133
75.2%
1 14560
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 44133
75.2%
1 14560
 
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44133
75.2%
1 14560
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44133
75.2%
1 14560
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44133
75.2%
1 14560
 
24.8%

Price_1
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3920744
Minimum1.1
Maximum1.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:42.495151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.21
Q11.34
median1.39
Q31.47
95-th percentile1.5
Maximum1.59
Range0.49
Interquartile range (IQR)0.13

Descriptive statistics

Standard deviation0.091138736
Coefficient of variation (CV)0.065469733
Kurtosis-0.17857602
Mean1.3920744
Median Absolute Deviation (MAD)0.07
Skewness-0.55560973
Sum81705.02
Variance0.0083062691
MonotonicityNot monotonic
2024-01-27T19:35:42.606743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1.47 7131
 
12.1%
1.39 6091
 
10.4%
1.35 6027
 
10.3%
1.5 4091
 
7.0%
1.33 3393
 
5.8%
1.34 3186
 
5.4%
1.48 3015
 
5.1%
1.49 2664
 
4.5%
1.46 2471
 
4.2%
1.37 2421
 
4.1%
Other values (27) 18203
31.0%
ValueCountFrequency (%)
1.1 158
 
0.3%
1.14 327
 
0.6%
1.17 116
 
0.2%
1.19 1102
1.9%
1.2 135
 
0.2%
1.21 2231
3.8%
1.22 62
 
0.1%
1.23 445
 
0.8%
1.24 378
 
0.6%
1.25 253
 
0.4%
ValueCountFrequency (%)
1.59 568
 
1.0%
1.52 678
 
1.2%
1.51 1354
 
2.3%
1.5 4091
7.0%
1.49 2664
 
4.5%
1.48 3015
5.1%
1.47 7131
12.1%
1.46 2471
 
4.2%
1.44 135
 
0.2%
1.43 502
 
0.9%

Price_2
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7809989
Minimum1.26
Maximum1.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:42.708822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.26
5-th percentile1.48
Q11.58
median1.88
Q31.89
95-th percentile1.9
Maximum1.9
Range0.64
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.17086769
Coefficient of variation (CV)0.095939242
Kurtosis0.6467319
Mean1.7809989
Median Absolute Deviation (MAD)0.02
Skewness-1.382152
Sum104532.17
Variance0.029195767
MonotonicityNot monotonic
2024-01-27T19:35:42.815273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1.89 20033
34.1%
1.9 7660
 
13.1%
1.57 4921
 
8.4%
1.88 3361
 
5.7%
1.87 2997
 
5.1%
1.85 2680
 
4.6%
1.51 1411
 
2.4%
1.58 1298
 
2.2%
1.56 1219
 
2.1%
1.86 1113
 
1.9%
Other values (20) 12000
20.4%
ValueCountFrequency (%)
1.26 875
1.5%
1.27 120
 
0.2%
1.31 200
 
0.3%
1.35 1089
1.9%
1.36 572
1.0%
1.46 69
 
0.1%
1.48 147
 
0.3%
1.49 513
 
0.9%
1.5 650
1.1%
1.51 1411
2.4%
ValueCountFrequency (%)
1.9 7660
 
13.1%
1.89 20033
34.1%
1.88 3361
 
5.7%
1.87 2997
 
5.1%
1.86 1113
 
1.9%
1.85 2680
 
4.6%
1.84 1096
 
1.9%
1.83 1053
 
1.8%
1.82 789
 
1.3%
1.81 882
 
1.5%

Price_3
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0067887
Minimum1.87
Maximum2.14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:42.918801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.87
5-th percentile1.93
Q11.97
median2.01
Q32.06
95-th percentile2.07
Maximum2.14
Range0.27
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.046867225
Coefficient of variation (CV)0.02335434
Kurtosis-0.28632204
Mean2.0067887
Median Absolute Deviation (MAD)0.04
Skewness-0.050864287
Sum117784.45
Variance0.0021965368
MonotonicityNot monotonic
2024-01-27T19:35:43.022934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.99 11053
18.8%
2.02 9563
16.3%
2.06 8148
13.9%
2.07 4962
8.5%
1.97 4580
7.8%
2.01 4512
7.7%
1.95 3860
 
6.6%
2 2305
 
3.9%
1.94 1919
 
3.3%
1.91 1664
 
2.8%
Other values (11) 6127
10.4%
ValueCountFrequency (%)
1.87 133
 
0.2%
1.89 501
 
0.9%
1.91 1664
 
2.8%
1.93 1007
 
1.7%
1.94 1919
 
3.3%
1.95 3860
 
6.6%
1.96 1167
 
2.0%
1.97 4580
7.8%
1.98 691
 
1.2%
1.99 11053
18.8%
ValueCountFrequency (%)
2.14 271
 
0.5%
2.13 52
 
0.1%
2.11 458
 
0.8%
2.09 1206
 
2.1%
2.07 4962
8.5%
2.06 8148
13.9%
2.05 581
 
1.0%
2.04 60
 
0.1%
2.02 9563
16.3%
2.01 4512
7.7%

Price_4
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1599453
Minimum1.76
Maximum2.26
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:43.123637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.76
5-th percentile1.97
Q12.12
median2.17
Q32.24
95-th percentile2.26
Maximum2.26
Range0.5
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.089824595
Coefficient of variation (CV)0.041586513
Kurtosis1.4148329
Mean2.1599453
Median Absolute Deviation (MAD)0.05
Skewness-1.3313374
Sum126773.67
Variance0.0080684578
MonotonicityNot monotonic
2024-01-27T19:35:43.230591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2.21 10899
18.6%
2.24 10872
18.5%
2.16 10830
18.5%
2.09 4891
8.3%
2.26 4655
7.9%
2.12 4481
7.6%
1.97 2198
 
3.7%
2.18 1869
 
3.2%
1.9 987
 
1.7%
2.15 736
 
1.3%
Other values (16) 6275
10.7%
ValueCountFrequency (%)
1.76 77
 
0.1%
1.89 563
 
1.0%
1.9 987
1.7%
1.94 578
 
1.0%
1.96 602
 
1.0%
1.97 2198
3.7%
1.98 654
 
1.1%
1.99 67
 
0.1%
2.02 483
 
0.8%
2.03 489
 
0.8%
ValueCountFrequency (%)
2.26 4655
7.9%
2.24 10872
18.5%
2.21 10899
18.6%
2.2 481
 
0.8%
2.19 507
 
0.9%
2.18 1869
 
3.2%
2.17 341
 
0.6%
2.16 10830
18.5%
2.15 736
 
1.3%
2.14 454
 
0.8%

Price_5
Real number (ℝ)

Distinct44
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6547977
Minimum2.11
Maximum2.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:43.339668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.11
5-th percentile2.44
Q12.63
median2.67
Q32.7
95-th percentile2.79
Maximum2.8
Range0.69
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.098271829
Coefficient of variation (CV)0.037016692
Kurtosis3.9071279
Mean2.6547977
Median Absolute Deviation (MAD)0.03
Skewness-1.5290992
Sum155818.04
Variance0.0096573523
MonotonicityNot monotonic
2024-01-27T19:35:43.457937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2.67 11794
20.1%
2.7 5582
 
9.5%
2.66 5207
 
8.9%
2.79 4347
 
7.4%
2.64 3615
 
6.2%
2.69 2491
 
4.2%
2.62 2334
 
4.0%
2.63 2217
 
3.8%
2.77 1698
 
2.9%
2.49 1677
 
2.9%
Other values (34) 17731
30.2%
ValueCountFrequency (%)
2.11 114
 
0.2%
2.19 80
 
0.1%
2.27 174
 
0.3%
2.29 56
 
0.1%
2.34 461
0.8%
2.36 271
0.5%
2.37 154
 
0.3%
2.38 96
 
0.2%
2.39 301
0.5%
2.4 480
0.8%
ValueCountFrequency (%)
2.8 1157
 
2.0%
2.79 4347
7.4%
2.78 868
 
1.5%
2.77 1698
 
2.9%
2.76 738
 
1.3%
2.75 380
 
0.6%
2.74 57
 
0.1%
2.73 741
 
1.3%
2.72 514
 
0.9%
2.71 1176
 
2.0%

Promotion_1
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
38512 
1
20181 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 38512
65.6%
1 20181
34.4%

Length

2024-01-27T19:35:43.573088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:43.655085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 38512
65.6%
1 20181
34.4%

Most occurring characters

ValueCountFrequency (%)
0 38512
65.6%
1 20181
34.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 38512
65.6%
1 20181
34.4%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 38512
65.6%
1 20181
34.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 38512
65.6%
1 20181
34.4%

Promotion_2
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
40169 
1
18524 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 40169
68.4%
1 18524
31.6%

Length

2024-01-27T19:35:43.753208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:43.836033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 40169
68.4%
1 18524
31.6%

Most occurring characters

ValueCountFrequency (%)
0 40169
68.4%
1 18524
31.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40169
68.4%
1 18524
31.6%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40169
68.4%
1 18524
31.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40169
68.4%
1 18524
31.6%

Promotion_3
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
56181 
1
 
2512

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 56181
95.7%
1 2512
 
4.3%

Length

2024-01-27T19:35:43.924519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:44.007401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 56181
95.7%
1 2512
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 56181
95.7%
1 2512
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56181
95.7%
1 2512
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56181
95.7%
1 2512
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56181
95.7%
1 2512
 
4.3%

Promotion_4
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
51776 
1
6917 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51776
88.2%
1 6917
 
11.8%

Length

2024-01-27T19:35:44.094845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:44.176302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51776
88.2%
1 6917
 
11.8%

Most occurring characters

ValueCountFrequency (%)
0 51776
88.2%
1 6917
 
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51776
88.2%
1 6917
 
11.8%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51776
88.2%
1 6917
 
11.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51776
88.2%
1 6917
 
11.8%

Promotion_5
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
56588 
1
 
2105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 56588
96.4%
1 2105
 
3.6%

Length

2024-01-27T19:35:44.265895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:44.347013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 56588
96.4%
1 2105
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 56588
96.4%
1 2105
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56588
96.4%
1 2105
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56588
96.4%
1 2105
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56588
96.4%
1 2105
 
3.6%

Sex
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
36044 
1
22649 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 36044
61.4%
1 22649
38.6%

Length

2024-01-27T19:35:44.434733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:44.516191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 36044
61.4%
1 22649
38.6%

Most occurring characters

ValueCountFrequency (%)
0 36044
61.4%
1 22649
38.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36044
61.4%
1 22649
38.6%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36044
61.4%
1 22649
38.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36044
61.4%
1 22649
38.6%

Marital status
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
35620 
1
23073 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35620
60.7%
1 23073
39.3%

Length

2024-01-27T19:35:44.605422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:44.687911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35620
60.7%
1 23073
39.3%

Most occurring characters

ValueCountFrequency (%)
0 35620
60.7%
1 23073
39.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35620
60.7%
1 23073
39.3%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 35620
60.7%
1 23073
39.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 35620
60.7%
1 23073
39.3%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.793962
Minimum18
Maximum75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:44.777807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile24
Q130
median36
Q346
95-th percentile63
Maximum75
Range57
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.052447
Coefficient of variation (CV)0.31067844
Kurtosis-0.2225012
Mean38.793962
Median Absolute Deviation (MAD)8
Skewness0.72314178
Sum2276934
Variance145.26149
MonotonicityNot monotonic
2024-01-27T19:35:44.889757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35 2859
 
4.9%
27 2859
 
4.9%
31 2759
 
4.7%
32 2487
 
4.2%
25 2436
 
4.2%
26 2403
 
4.1%
40 2265
 
3.9%
37 2155
 
3.7%
36 2011
 
3.4%
33 1931
 
3.3%
Other values (46) 34528
58.8%
ValueCountFrequency (%)
18 235
 
0.4%
19 106
 
0.2%
20 196
 
0.3%
21 467
 
0.8%
22 319
 
0.5%
23 1251
2.1%
24 1852
3.2%
25 2436
4.2%
26 2403
4.1%
27 2859
4.9%
ValueCountFrequency (%)
75 72
 
0.1%
74 94
 
0.2%
73 121
 
0.2%
71 101
 
0.2%
70 92
 
0.2%
68 269
 
0.5%
67 457
0.8%
66 357
0.6%
65 714
1.2%
64 546
0.9%

Education
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
1
37161 
2
11716 
0
8462 
3
 
1354

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 37161
63.3%
2 11716
 
20.0%
0 8462
 
14.4%
3 1354
 
2.3%

Length

2024-01-27T19:35:44.999590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:45.085468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 37161
63.3%
2 11716
 
20.0%
0 8462
 
14.4%
3 1354
 
2.3%

Most occurring characters

ValueCountFrequency (%)
1 37161
63.3%
2 11716
 
20.0%
0 8462
 
14.4%
3 1354
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 37161
63.3%
2 11716
 
20.0%
0 8462
 
14.4%
3 1354
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 37161
63.3%
2 11716
 
20.0%
0 8462
 
14.4%
3 1354
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 37161
63.3%
2 11716
 
20.0%
0 8462
 
14.4%
3 1354
 
2.3%

Income
Real number (ℝ)

HIGH CORRELATION 

Distinct499
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121841.64
Minimum38247
Maximum309364
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size458.7 KiB
2024-01-27T19:35:45.183918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38247
5-th percentile68834
Q195541
median117971
Q3138525
95-th percentile194882
Maximum309364
Range271117
Interquartile range (IQR)42984

Descriptive statistics

Standard deviation40643.741
Coefficient of variation (CV)0.3335784
Kurtosis3.8683217
Mean121841.64
Median Absolute Deviation (MAD)21506
Skewness1.4248713
Sum7.1512516 × 109
Variance1.6519137 × 109
MonotonicityNot monotonic
2024-01-27T19:35:45.302913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
124597 358
 
0.6%
106205 353
 
0.6%
158193 347
 
0.6%
69487 228
 
0.4%
135275 182
 
0.3%
113012 179
 
0.3%
123003 179
 
0.3%
95438 178
 
0.3%
147626 178
 
0.3%
81200 175
 
0.3%
Other values (489) 56336
96.0%
ValueCountFrequency (%)
38247 114
0.2%
43684 129
0.2%
43805 98
0.2%
53608 93
0.2%
57480 118
0.2%
58207 101
0.2%
60868 96
0.2%
61824 108
0.2%
62263 94
0.2%
62335 92
0.2%
ValueCountFrequency (%)
309364 105
0.2%
308529 109
0.2%
308491 125
0.2%
281923 135
0.2%
281084 123
0.2%
279593 145
0.2%
273063 77
0.1%
268340 103
0.2%
267872 122
0.2%
260847 102
0.2%

Occupation
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
1
29882 
0
21032 
2
7779 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 29882
50.9%
0 21032
35.8%
2 7779
 
13.3%

Length

2024-01-27T19:35:45.413458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:45.498241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29882
50.9%
0 21032
35.8%
2 7779
 
13.3%

Most occurring characters

ValueCountFrequency (%)
1 29882
50.9%
0 21032
35.8%
2 7779
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 29882
50.9%
0 21032
35.8%
2 7779
 
13.3%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 29882
50.9%
0 21032
35.8%
2 7779
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 29882
50.9%
0 21032
35.8%
2 7779
 
13.3%

Settlement size
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size458.7 KiB
0
32081 
1
14727 
2
11885 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters58693
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32081
54.7%
1 14727
25.1%
2 11885
 
20.2%

Length

2024-01-27T19:35:45.591101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-27T19:35:45.675059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32081
54.7%
1 14727
25.1%
2 11885
 
20.2%

Most occurring characters

ValueCountFrequency (%)
0 32081
54.7%
1 14727
25.1%
2 11885
 
20.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58693
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32081
54.7%
1 14727
25.1%
2 11885
 
20.2%

Most occurring scripts

ValueCountFrequency (%)
Common 58693
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32081
54.7%
1 14727
25.1%
2 11885
 
20.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58693
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32081
54.7%
1 14727
25.1%
2 11885
 
20.2%

Interactions

2024-01-27T19:35:38.309175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:27.874944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.912515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.871227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.792646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.742920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.669775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.605677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.552693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.495282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.419709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.390007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.386762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:27.998903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.990736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.947661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.870780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.819886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.747600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.683865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.631065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.572437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.500732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.466485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.467426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.124029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.070889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.026605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.952617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.898710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.829506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.767137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.715448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.652003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.583815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.545744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.542902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.207387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.148984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.101099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.029414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.973824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.905279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.843818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.792045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.726862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.661902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.620677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.623597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.287326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.231304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.178664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.110888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.052351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.985501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.925168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.872182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.805841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.744466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.698933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.700320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.362895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.309343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.253828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.188936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.127557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.062362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.003098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.948964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.881375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.822873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.775409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.779547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.440334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.388449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.330145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.267894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.203948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.138135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.081267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.025910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.957981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.903673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.850922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.858822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.519184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.469940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.408733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.349032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.283221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.217560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.160179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.106450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.036518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.986810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.929955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.935934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.596421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.551995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.485147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.428860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.359686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.294984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.238851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.183211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.112046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.067949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.005735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:40.267266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.670882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.629895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.559778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.504951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.434866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.370849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.314716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.258330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.185589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.146330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.080202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:40.349617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.758513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.713790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.641125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.587367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.516919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.452146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.397415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.340795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.265617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.230044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.159619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:40.425819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:28.834545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:29.792989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:30.716015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:31.665180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:32.592640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:33.528862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:34.474853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:35.417611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:36.340925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:37.308967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-27T19:35:38.233367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-27T19:35:45.748792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AgeBrandDayEducationIDIncidenceIncomeLast_Inc_BrandLast_Inc_QuantityMarital statusOccupationPrice_1Price_2Price_3Price_4Price_5Promotion_1Promotion_2Promotion_3Promotion_4Promotion_5QuantitySettlement sizeSex
Age1.0000.093-0.0000.5260.0510.1090.4000.0930.1090.2460.2030.0030.000-0.0030.0010.0040.0080.0000.0000.0000.0000.0700.2370.207
Brand0.0931.0000.0230.1840.0401.0000.1050.2530.2460.1620.157-0.043-0.037-0.0030.006-0.0120.0360.0710.0380.0280.0650.9680.2150.174
Day-0.0000.0231.0000.0000.0040.069-0.0010.0340.0760.0000.000-0.101-0.3260.7950.7960.4550.2450.1890.3340.2970.2020.0330.0000.000
Education0.5260.1840.0001.0000.1090.0660.3690.0590.0660.4090.1660.0030.004-0.0030.0010.0020.0000.0020.0000.0000.0000.0550.2070.220
ID0.0510.0400.0040.1091.0000.0690.0740.0400.0690.1810.1210.0030.0030.0040.0040.0050.0000.0000.0000.0000.0000.0660.1580.173
Incidence0.1091.0000.0690.0660.0691.0000.0820.2290.2120.0080.084-0.049-0.0460.0030.012-0.0060.0340.0420.0000.0180.0290.9870.1120.034
Income0.4000.105-0.0010.3690.0740.0821.0000.1050.1110.1660.6910.0030.004-0.0050.0020.0030.0000.0000.0000.0000.0000.0790.4310.230
Last_Inc_Brand0.0930.2530.0340.0590.0400.2290.1051.0001.0000.1620.157-0.040-0.0050.0110.035-0.0140.0630.0070.0180.0140.0140.2190.2140.173
Last_Inc_Quantity0.1090.2460.0760.0660.0690.2120.1111.0001.0000.0080.085-0.042-0.0070.0150.041-0.0090.0400.0000.0110.0080.0000.2050.1120.034
Marital status0.2460.1620.0000.4090.1810.0080.1660.1620.0081.0000.1030.0040.0060.0000.001-0.0000.0050.0000.0000.0000.000-0.0030.1320.458
Occupation0.2030.1570.0000.1660.1210.0840.6910.1570.0850.1031.0000.0030.007-0.0060.0010.0000.0000.0000.0040.0000.0000.0620.4270.087
Price_10.003-0.043-0.1010.0030.003-0.0490.003-0.040-0.0420.0040.0031.0000.145-0.011-0.1440.0820.5470.3020.1920.3980.218-0.0520.0000.000
Price_20.000-0.037-0.3260.0040.003-0.0460.004-0.005-0.0070.0060.0070.1451.000-0.213-0.228-0.1810.2370.5070.1770.1180.099-0.0510.0060.010
Price_3-0.003-0.0030.795-0.0030.0040.003-0.0050.0110.0150.000-0.006-0.011-0.2131.0000.6610.2710.3760.1880.2050.2200.1450.0040.0000.000
Price_40.0010.0060.7960.0010.0040.0120.0020.0350.0410.0010.001-0.144-0.2280.6611.0000.3040.2140.1950.3150.6100.2940.0140.0000.000
Price_50.004-0.0120.4550.0020.005-0.0060.003-0.014-0.009-0.0000.0000.082-0.1810.2710.3041.0000.2060.2200.2410.2180.390-0.0040.0000.000
Promotion_10.0080.0360.2450.0000.0000.0340.0000.0630.0400.0050.0000.5470.2370.3760.2140.2061.0000.1370.1550.0730.0770.0360.0000.000
Promotion_20.0000.0710.1890.0020.0000.0420.0000.0070.0000.0000.0000.3020.5070.1880.1950.2200.1371.0000.0600.0330.1010.0450.0000.000
Promotion_30.0000.0380.3340.0000.0000.0000.0000.0180.0110.0000.0040.1920.1770.2050.3150.2410.1550.0601.0000.0770.0050.0050.0080.000
Promotion_40.0000.0280.2970.0000.0000.0180.0000.0140.0080.0000.0000.3980.1180.2200.6100.2180.0730.0330.0771.0000.1320.0190.0000.000
Promotion_50.0000.0650.2020.0000.0000.0290.0000.0140.0000.0000.0000.2180.0990.1450.2940.3900.0770.1010.0050.1321.0000.0270.0000.006
Quantity0.0700.9680.0330.0550.0660.9870.0790.2190.205-0.0030.062-0.052-0.0510.0040.014-0.0040.0360.0450.0050.0190.0271.0000.0460.019
Settlement size0.2370.2150.0000.2070.1580.1120.4310.2140.1120.1320.4270.0000.0060.0000.0000.0000.0000.0000.0080.0000.0000.0461.0000.211
Sex0.2070.1740.0000.2200.1730.0340.2300.1730.0340.4580.0870.0000.0100.0000.0000.0000.0000.0000.0000.0000.0060.0190.2111.000

Missing values

2024-01-27T19:35:40.546366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-27T19:35:40.824084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

IDDayIncidenceBrandQuantityLast_Inc_BrandLast_Inc_QuantityPrice_1Price_2Price_3Price_4Price_5Promotion_1Promotion_2Promotion_3Promotion_4Promotion_5SexMarital statusAgeEducationIncomeOccupationSettlement size
02000000011000001.591.872.012.092.66010000047111086610
120000000111000001.511.891.992.092.66000000047111086610
220000000112000001.511.891.992.092.66000000047111086610
320000000116000001.521.891.982.092.66000000047111086610
420000000118000001.521.891.992.092.66000000047111086610
520000000123000001.501.901.992.092.66000000047111086610
620000000128122001.501.901.992.092.67000000047111086610
720000000137000211.501.901.992.092.67000000047111086610
820000000141000001.351.581.972.092.67111000047111086610
920000000143000001.351.581.972.092.67111000047111086610
IDDayIncidenceBrandQuantityLast_Inc_BrandLast_Inc_QuantityPrice_1Price_2Price_3Price_4Price_5Promotion_1Promotion_2Promotion_3Promotion_4Promotion_5SexMarital statusAgeEducationIncomeOccupationSettlement size
58683200000500681000001.421.852.062.242.77110000042112094610
58684200000500689000001.501.872.062.242.78000000042112094610
58685200000500693000001.421.512.022.242.77010000042112094610
58686200000500694000001.421.512.022.242.77010000042112094610
58687200000500697126001.421.511.972.242.78000000042112094610
58688200000500703000211.411.852.012.242.79001000042112094610
58689200000500710000001.361.842.092.242.77000000042112094610
58690200000500717000001.501.802.142.242.75000000042112094610
58691200000500722123001.511.822.092.242.80000000042112094610
58692200000500726000211.511.822.092.242.80000000042112094610